Data Modeling Techniques

Copyright Global Data Strategy, Ltd. 2021
Data Modeling Techniques
Donna Burbank
Global Data Strategy, Ltd.
September 23, 2021
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2021
Donna Burbank
2
• Recognized industry expert in information
management with over 25 years of
experience in data strategy, information
management, data modeling, metadata
management, and enterprise architecture
• Managing Director at Global Data Strategy,
Ltd., an international information
management consulting company that
specializes in the alignment of business
drivers with data-centric technology
• Worked with dozens of Fortune 500
companies worldwide in the Americas,
Europe, Asia, and Africa and speaks
regularly at industry conferences
• Excellence in Data Management Award
from DAMA International
• Past President and Advisor to the DAMA
Rocky Mountain chapter
• Co-author of several books on data
management
• Regular contributor to industry
publications
• She can be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, US
Follow on Twitter @donnaburbank
@GlobalDataStrat

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Data Modeling Case Study – Business Data Modeling at Kiewit
• April Master Data Management – Aligning Data, Process, and Governance
• May Data Architecture, Solution Architecture, Platform Architecture – What’s the Difference?
• June Enterprise Architecture vs. Data Architecture
• July Best Practices in Metadata Management
• August Data Quality Best Practices (with guest Nigel Turner)
• September Data Modeling Techniques
• October Data Governance: Aligning Technical & Business Approaches
• December Data Architecture for Digital Transformation
3
This Year’s Lineup

What We’ll Cover Today
• Data modeling continues to be a tried-and-true
method of managing critical data aspects from
both the business and technical perspective.
• Like any tool or methodology, there is a “right tool
for the right job”, and specific model types exist for
both business and technical users across
operational, reporting, analytic, and other use
cases.
• This webinar will provide an overview of the
various data modeling techniques available, and
how to use each for maximum value to the
organization.
4

Global Data Strategy, Ltd. 2021 5
A Successful Data Strategy links Business Goals with Technology Solutions
“Top-Down” alignment with
business priorities
“Bottom-Up” management &
inventory of data sources
Managing the people, process,
policies & culture around data
Coordinating & integrating
disparate data sources
Leveraging & managing data for
strategic advantage
Data Modeling is Part of a Wider Data Strategy
www.globaldatastrategy.com

Third Normal Form is the only
way to go!!
I store everything in key-value
pairs!!

Both And

Each Tool Has Its Purpose
8
You wouldn’t argue whether a wrench is “better” than
a hammer … they clearly have different purposes.
… the same is true for data models.

Different Physical Models for Different Use Cases
9
Relational – Normal Form
• Reduce redundancy for
operational data
• Increase data quality
• Ensure consistency (ACID
transactions)
Dimensional– Star Schema
• Ease of reporting for summarized
and historical data
• Ability to easily “slice and dice” for
self-service reporting
• Performance and flexibility
NoSQL
No modeling technique is inherently “better” than another. Data use cases & purpose drives what “good” looks like.
…Rant over…
• Speed of retrieval, low
latency
• High data volumes
• Flexibility for change
…And More!
• There are numerous
ways to model and store
data.
• Hierarchical/XML
• COBOL Copybook!
• S3 “buckets”
• Data Vault
• Etc…

In a Typical Organization,
there are many Use Cases for Data Models
10
Web
Application
Operational
System
NoSQL Key Value Pair
for web session info
Relational Database
for Operational Data.
The following is just a subset of options that exist….
Operational Usage Transfer /
Exchange
JSON
XML
… Etc.
Storage for Analytics /
Reporting
Relational for Consistency
& Standards
Reporting for Analytic
“Slicing & Dicing”
Data Vault for Flexible
Storage
Consumption for Analytics
& Reporting
Cubes
Cubes for Business
Intelligence Reporting
Flattened Tables
Flattened tables for
Analytics & Data Science
Master Data & Hierarchies
for Data Quality &
Consistencye
Graph Database
Graph Database for
Connections & Patterns

A little data modeling up-front
… prevents headaches down the road
From Data Modeling for the Business by Hoberman, Burbank, Bradley, Technics Publications, 2009
• It’s often tempting to skip data
modeling documentation because it’s
“faster”
• But…long-term, it’s ultimately longer as
errors and inconsistencies need to be
fixed as a result.
“If you don’t have time to do it right, do
you have time to do it again?”

Levels of Data Models
12
Conceptual
Logical
Physical
Purpose
Communication & Definition of
Business Concepts & Rules
Clarification & Detail
of Business Rules &
Data Structures
Technical
Implementation on
a Physical Database
Audience
Business Stakeholders
Data Architects
Data Architects
Business Analysts
DBAs
Developers
Business Concepts
Data Entities
Physical Tables
Business Stakeholders
Data Architects
Enterprise
Subject Areas
Organization & Scoping of main
business domain areas

Business Data Model (Conceptual)
• Communication & definition of core data concepts & their definitions
• A business data model
provides core definitions
of key data objects.
• It also shows key
relationships between
data objects.
• Even a simple diagram as
the one on the right can
tell a powerful “story”
…. And uncover key
business issues and
opportunities.
• How do we define a
“customer” vs. a “client”
• Is our employee
relationship different for
each?

Use the Language of Your Audience
• When communicating with business stakeholders, it’s important to display data models in a way
that’s intuitive to them
• PowerPoint-style Conceptual Data Models
• Use Business Terminology
• Avoid Excess Detail
14
Gaining Buy-In

Tell a Story
• Humans have evolved over time as storytellers
• We can’t even sleep without dreaming in stories.
• No one cares about your data model…
• … but they do care about the RESULTS of your data model
• … relate the model to a real world impact or scenario..i.e.
“story”
15
What impact does the data model have on the business?
Humans are Storytellers.

Real-World Quotations from Business Users
16

Logical Data Model
• A logical data model
• Defines detailed business rules
• Includes attributes, data types,
nullability, etc.
• Defines data structures, but not
physical tables (e.g. hierarchies)
…. And uncovers key
business issues and opportunities.
• Can a customer have more than
one address?
• Is Fax number still a required field?
• What is a fax number???....
Place
Appear on
Contain
Belong to
Customer
customer identifier
first name
middle initial
last name
description
Product
product identifier
product name
description
Order
customer identifier (FK)
product identifier (FK)
order date
Product Part Combination
product identifier (FK)
part identifier (FK)
Raw Material
material_identifier
Finished Good
finished good identifier
Subassembly
subassembly identifier
Part
part identifier
part name
description

Environment Agency**
• The Environment Agency worked with Global Data Strategy to develop Data Models & Data Standards in
order to support Open Data publication of key environmental measures.
18
Governing Open Data Publication through Data Models
• Land boundaries
• Air & Water Quality
• Fish & Wildlife populations
• Etc.
• Common Data Models & Standards helped create a
common lingua franca across the organization:
• Saving time & money
• Supporting Regulation
• Enhancing public reputation
• Improving data quality & consistency
• Increasing collaboration between teams
“Establishing a standard is a really important step in bringing our
information together so we can be better joined up, better integrated and
work together more efficiently. In short, if you’ve got even the
slightest interest in how we plan and deliver outcomes on the ground, you
should be taking an interest in this!”
- National River Basin Operations Manager, Environment Agency
** See March 2019 DATAVERSITY webinar

3
Architecture vs. Construction
• It’s a common analogy to use building architecture as an analogy to data architecture.
• When constructing a building, there is a clear distinction between designing a house and building a house.
19
Design Build
** See March 2021 DATAVERSITY webinar for construction case study using data models++

3
Architecture vs. Construction
20
• When constructing a database, there is a clear distinction between
designing and building.
Design Build

Physical Data Model
21
• A physical data model
• Defines data structures to store
data on a physical platform (e.g.
RDMBS, Document data store,
etc.)
• Optimizes for performance,
query, etc.
… And ensures that data is stored
in a fit for purpose manner. e.g.
How can I:
• Store data to reduce redundancy
and increase data quality?
• Optimize data storage to “slice
and dice” for self-service
analytics?
• Optimize data storage for speed
of query?

Data Modeling Creates an “Active Inventory” of Data Assets
• Know what data you have: Create a visual inventory of database systems
• Know what your data means: Communicate key business requirements between business and IT stakeholders
• Support data consistency: Build consistent database structures & support data governance initiatives
Sybase
MySQL
Oracle
Data Models
Teradata
Sybase
SQL
Server
DB2
Teradata
SQL
Server DB2
MySQL
SQL
Azure
SQL
Azure
Oracle
Forward Engineering Reverse Engineering

Real-World Use Cases for Data Models
23
Examples from practice
Environmental Data
Sampling
How do we align our
scientific terminology?
These are all from real-world examples we’ve implemented. Data Models are everywhere.
Early Childhood
Development
How to we create better
outcomes for children?
eCommerce &
Digital Transformation
What is data is key to our
digital transformation?
University Student
Support
Understanding the Student
Journey with Data
Water Utility
Data Modernization
How do we reflect our
business rules into our new,
digital environment?
Construction
Contracting Efficiencies
How can a data model
highlight inefficiencies in
our business?
Agile Software
Development
What data is involved in
this user story?
Membership Org
Customer Centricity
How do we define our
customer/member?

Summary
• There is no “One size fits all” for data modeling
techniques
• Consider your audience: Business or Technical
• Consider your purpose: Reporting, Operational, etc.
• Whatever the style used, data models typically help
promote:
• Efficiency
• Standardization
• Collaboration

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Data Modeling Case Study – Business Data Modeling at Kiewit
• April Master Data Management – Aligning Data, Process, and Governance
• May Data Architecture, Solution Architecture, Platform Architecture – What’s the Difference?
• June Enterprise Architecture vs. Data Architecture
• July Best Practices in Metadata Management
• August Data Quality Best Practices (with guest Nigel Turner)
• September Data Modeling Techniques
• October Data Governance: Aligning Technical & Business Approaches
• December Data Architecture for Digital Transformation
25
This Year’s Lineup

Who We Are: Business-Focused Data Strategy
Maximize the Organizational Value of Your Data Investment
In today’s business environment, showing rapid time to value for
any technical investment is critical.
But technology and data can be complex. At Global Data Strategy,
we help demystify technical complexity to help you:
• Demonstrate the ROI and business value of data to your
management
• Build a data strategy at your pace to match your unique culture
and organizational style.
• Create an actionable roadmap for “quick wins”, which building
towards a long-term scalable architecture.
Global Data Strategy’s shares experience from some of the largest
international organizations scaled to the pace of your unique team.
www.globaldatastrategy.com
Global Data Strategy has worked with organizations globally in the
following industries:
Finance · Retail · Social Services · Health Care · Education · Manufacturing
· Government · Public Utilities · Construction · Media & Entertainment ·
Insurance …. and more

Global Data Strategy, Ltd. 2021 www.globaldatastrategy.com
Questions?
Thoughts? Ideas?
27

Data Modeling Techniques

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